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<table width="100%" summary="page for finney71"><tr><td>finney71</td><td style="text-align: right;">R Documentation</td></tr></table>

<h2>Example from Finney (1971)</h2>

<h3>Description</h3>

<p>For each of six concentration of an insecticid the number of insects affected (out of the number of insects)
was recorded.
</p>


<h3>Usage</h3>

<pre>data(finney71)</pre>


<h3>Format</h3>

<p>A data frame with 6 observations on the following 3 variables.
</p>

<dl>
<dt><code>dose</code></dt><dd><p>a numeric vector</p>
</dd>
<dt><code>total</code></dt><dd><p>a numeric vector</p>
</dd>
<dt><code>affected</code></dt><dd><p>a numeric vector</p>
</dd>
</dl>



<h3>Source</h3>

<p>Finney, D. J. (1971) <em>Probit Analysis</em>, Cambridge: Cambridge University Press.
</p>


<h3>Examples</h3>

<pre>

## Model with ED50 as a parameter
finney71.m1 &lt;- drm(affected/total ~ dose, weights = total,
data = finney71, fct = LL.2(), type = "binomial")

summary(finney71.m1)
plot(finney71.m1, broken = TRUE, bp = 0.1, lwd = 2)

ED(finney71.m1, c(10, 20, 50), interval = "delta", reference = "control")

## Model fitted with 'glm'
#fitl.glm &lt;- glm(cbind(affected, total-affected) ~ log(dose),
#family=binomial(link = logit), data=finney71[finney71$dose != 0, ])
#summary(fitl.glm)  # p-value almost agree for the b parameter
#
#xp &lt;- dose.p(fitl.glm, p=c(0.50, 0.90, 0.95))  # from MASS
#xp.ci &lt;- xp + attr(xp, "SE") %*% matrix(qnorm(1 - 0.05/2)*c(-1,1), nrow=1)
#zp.est &lt;- exp(cbind(xp.ci[,1],xp,xp.ci[,2]))
#dimnames(zp.est)[[2]] &lt;- c("zp.lcl","zp","zp.ucl")
#zp.est  # not far from above results with 'ED'

## Model with log(ED50) as a parameter
finney71.m2 &lt;- drm(affected/total ~ dose, weights = total,
data = finney71, fct = LL2.2(), type = "binomial")

## Confidence intervals based on back-transformation
##  complete agreement with results based on 'glm'
ED(finney71.m2, c(10, 20, 50), interval = "fls", reference = "control")

</pre>


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